Wide-band image enhancement

ABSTRACT

An image processing technique produces modified images by extracting strong features of the original image, i.e., bars and edges, and superimposing such extracted features onto the original image. The invention combines the Hilbert transform of the image data with the image data in a pre-defined manner to produce the so-called energy function whose maxima correspond to the strong features of the image. Addition of these extracted features to the original image results in obtaining an enhanced image. In addition, the invention provides techniques for enhancing the real-world view of natural scenes. Another practice of the invention employs a plurality of oriented filters for extracting luminance features of an image. An apparatus of the invention displays the extracted luminance features as contour version of the original image. Another apparatus of the invention provides a minified contour image of a natural scene to assist a patient having a restricted peripheral vision in locating objects in the scene.

RELATED APPLICATIONS

The present application is a continuation-in-part of a PCT applicationhaving Ser. No. PCT/US98/23933, filed on Nov. 10, 1998, which claimspriority to U.S. provisional applications Serial No. 60/065,297, filedon Nov. 13, 1997, and Ser. No. 60/070,122, filed on Dec. 31, 1997.

BACKGROUND

This invention relates to methods and apparatus for image processing andmore particularly to image enhancement. In particular, the inventionrelates to methods and apparatus for the enhancement of both videoimages of natural scenes that contain a wide range of spatialfrequencies and of real-world views of natural scenes.

Traditional image enhancement methods suffer from a number of drawbacks.Many traditional image enhancement methods can not effectively enhanceimages over a wide band of spatial frequencies. For example, onetechnique enhances an image by changing its spatial frequency contentthrough manipulation of the coefficients of a discrete cosine transform(“DCT”) of the image. The method segments the image into 8×8 pixelsections and obtains the cosine transform of each section. Thistechnique fails to capture low frequency components that arise as aresult of features that have significant variations in luminance mainlyover an area larger than an 8×8 pixel section. Other techniques processan image in the spatial domain. Such techniques typically enhance theimage only over a narrow range of frequencies. See E. Peli, “LimitationsOf Image Enhancement For The Visually Impaired,” Optometry and VisionScience, vol. 6, pp. 15″24 (1992); and E. Peli, E. Lee, C. L. Trempe, S.Buzney, “Image Enhancement For The Visually Impaired: The Effects OfEnhancement On Face Recognition”, Journal of Optical Society of America,vol. 11 pp. 1929-1939 (1994).

Therefore, such traditional image enhancement techniques are notsuitable for enhancing the images of many natural scenes that contain awide range of spatial frequencies. Further, human observers detectmoving objects that contain a wide band of frequencies more readily thanthose with a narrow band of frequencies. Thus, the traditionaltechniques are not appropriate in systems for assisting detection ofmoving objects, or in systems that provide real-time viewing enhancementof natural scenes.

Traditional methods also can not readily enhance an image while the sizeof the image changes. For example, the viewer of a digital televisiondisplay could desire to follow the image of an object that undergoes alarge change in its size while maintaining a selected degree ofenhancement. The ability to enhance a wide range of frequencies iscrucial in such applications. Traditional techniques, such as a DCTmethod or other band-limited methods are not appropriate for suchapplications because they provide a limited range of spatial frequenciesof the image.

In addition, traditional enhancement methods, both in the spatial domainand in the frequency domain, typically manipulate a large fraction ofpixels. As a result, their use in the enhancement of color picturesrequires tracking the color content of many pixels while the computationchanges the luminance of those pixels.

Accordingly, it is an object of this invention to provide methods andapparatus for enhancing images over a wide band of spatial frequencies.

It is another object of the invention to provide methods and apparatusthat can readily enhance such images over a reasonable range of imagesizes.

It is yet another object of the invention to provide methods andapparatus for real-time viewing enhancement of natural scenes.

It is a further object of the invention to provide methods and apparatusfor better enhancement of color pictures.

It is yet a further object of the invention to provide methods andapparatus for expanding field of view of a patient suffering fromperipheral field loss.

The invention is next described in connection with illustratedembodiments. It will, however, be obvious to those skilled in the artthat various modifications can be made in the embodiments withoutdeparting from the spirit or scope of this invention.

SUMMARY OF THE INVENTION

The methods and apparatus according to this invention modify an imageby 1) locating certain features of the image, such as the boundaries ofobjects in the image, 2) manipulating such located features to obtainmodified features, and 3) adding the modified features to the originalimage. In particular, one embodiment of the invention employs atwo-dimensional Hilbert transform of the image data to create atwo-dimensional function, a so-called energy function, whose localmaxima correspond to points lying on the boundaries between regions ofmarked difference in luminance, i.e., edges, or to points correspondingto peaks or troughs in luminance, i.e., bars. The invention furtherprovides techniques to interconnect these maxima, thus delineating thedesired features.

An application of this invention is to improve the visibility of videoimages for people with visual impairment, e.g., cataracts or maculardegeneration. In particular, one embodiment of the present inventionallows real-time image processing and enhancement of the real-world viewfor the visually impaired. This embodiment includes a dedicatedmicroprocessor, programmed to extract the boundaries of objects in thefield of view, according to the methods of the invention from datainputted from a digital camera. This embodiment also incorporates videoequipment to project extracted features onto screens. These screens canbe integrated in a wearable real-time image enhancement apparatus, suchas a head mounted display (“HMD”) display unit.

Another application enhances the real-world view, under reducedvisibility conditions such as fog, by projecting the enhanced features,obtained from non-visual sensors, e.g., infrared or radar, on heads-updisplays (HUD) of an airplane or of a car windshield. Anotherapplication of this invention is to improve the visibility of televisionimages for individuals with visual impairment. Yet other applicationsrelate to the enhancement of satellite and reconnaissance pictures orother military imaging devices, and to the delineation of features ofinterest in such pictures.

The invention is typically practiced on a digital image that consists ofa discrete two-dimensional map of luminance. Some embodiments of theinvention represent such images by two dimensional matrices. Theinvention employs an extension of well known methods for calculating theHilbert transform of a function in one dimension to obtain a discretetwo-dimensional Hilbert transform of a function of the image data.

It is well understood that the one-dimensional Hilbert transform of afunction of a single variable can be calculated by 1) obtaining theFourier transform of the function, 2) obtaining a modified transformfunction whose values are zero at points where its independent variableis less than zero, and whose values are those of the Fourier transformat points where its independent variable is larger than zero. A thirdstep is to obtain the inverse transform of this modified transformfunction.

One preferred embodiment of the invention obtains the two-dimensionalHilbert transform of the image data by 1) computing the two-dimensionalFourier transform of the image, 2) obtaining a new two-dimensionaltransform function whose values in a selected arbitrary contiguous halfof the two-dimensional Fourier plane are zero, and whose valuescorrespond to those of the two-dimensional Fourier transform of theimage in the other half, and 3) obtaining the inverse Fourier transformof the modified transform function. The real part of the complex inverseFourier transform of the modified transform function corresponds to theoriginal image and the imaginary part corresponds to the Hilberttransform of the image.

One preferred embodiment of the invention combines the image data withthe Hilbert transform of the image data to obtain a new two-dimensionalfunction, a so-called energy function. In particular, the procedure forforming the energy function calls for obtaining the square root of thePythagorean sum of the image data and of the values of the Hilberttransform at each point, e.g., at each pixel of a digital image.

One embodiment of the invention utilizes the positions of the peaks ofthe energy function to locate the visually relevant luminance featuresof the image. It is understood that such peaks correspond to peaks ortroughs in luminance, or to those locations in the original image wherechanges in image intensity profile occur because of the existence ofmaximal phase congruency among the various Fourier components of theimage.

The local maxima of the energy function correspond to points of bothminimum and of maximum intensity in the original image data, and also tothe boundaries between regions of low and of high luminance. It is notreasonably feasible to classify the maxima of the energy function withrespect to the polarity of the corresponding points in the image databased purely on the energy function. Thus, some embodiments of theinvention implement a further examination of the image data at eachpoint that corresponds to a maximum of the energy function to label thepolarity of each such maximum.

One aspect of the present invention relates to the creation of a map ofdots corresponding to the points designated as the maxima of the energyfunction. The invention optionally employs methods known in the art toconnect these dots to produce lines corresponding to the desiredfeatures. In addition, the invention provides the capability ofmanipulating these lines by widening them through convolution with anappropriate windowing function, e.g., a Gaussian with a selected width,or manipulating their intensities, to improve the contrast of the image.

Some embodiments of the invention employ only one arbitrarily selectedpolarity, i.e., either dark or bright, to display the dots or thecontour lines at edges, whereas other embodiments utilize twopolarities. A bipolar representation displays an edge with two dots, onedark and the other bright, next to each other. Some embodiments thatutilize a bipolar representation examine the unmodified image to selecta choice for juxtaposition of the dark and bright dots that correspondsto the sense of the transition of luminance at the correspondinglocation of the image. Both embodiments represent the polarity of barsin accordance with the polarity in the original image. Other embodimentsof the invention use only a single polarity of dots, i.e., light ordark, to represent all bars or edges.

A preferred embodiment of the invention superimposes these modifiedcontour lines onto the original image to obtain a new image in whichcertain features have been modified, e.g., the boundaries of the objectsin the image have been enhanced.

The invention can also enhance color images. Because the inventionmanipulates only a limited number of pixels, i.e., those correspondingto the visually relevant features of the image, only a few pixels changecolor due to the enhancement. Thus, the methods of the invention arebetter in preserving the color of an image than other enhancingtechniques.

An alternative method for locating luminance features, such as edgesand/or bars, of an image and their polarities employs a plurality oforiented filters to extract the luminance features. The application of anumber of filters having different center frequencies, and optionallydifferent bandwidths, to the image provides a plurality of filteredimages. One preferred practice of this aspect of the invention assignsto each pixel of a filtered image either a dark, a light, or a grayscale in a manner described below, to provide an assigned image. Acontour constructor, such as a programmable digital processor, receivesthe assigned images, and obtains the features of the original image byapplication of a set of pre-programmed steps to the assigned images, asdescribed further below.

In another aspect, the invention expands the field of view of patientssuffering from loss in their peripheral vision. In particular, onepractice of this aspect of the invention obtains a spatially minifiedimage of the natural scene, and extracts the luminance features of theminified image. Video equipment projects the extracted features on atransparent screen disposed before at least one eye of the patient. Thepatient can readily locate objects in the minified image, and view thelocated objects directly by scanning her eyes and/or by moving her head.

Another aspect of the invention relates to providing a night visiondevice that facilitates night mobility of patients who suffer from aloss of night vision. In particular, one practice of the inventionobtains an infrared image of a natural scene, extracts the contours ofobjects in the image according to the teachings of the presentinvention, and projects the contours onto a see-through visual screendisposed in front of at least one of the patient's eyes.

One further practice of the invention relates to intraocularimplantation of a telescope that is configured as a minifier into apatient's eye, to provide a wide-angle minified view of a natural scene.Such a minified view helps the mobility of a patient suffering fromperipheral field loss.

Thus, the invention attains the objectives set forth above by extractingvisually relevant features of an image, manipulating these features toobtain modified features, and superimposing such modified features ontothe original image to obtain a modified image.

These and other features of the invention are more fully set forth belowwith reference to the detailed description of illustrated embodiments,and the accompanying drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart depicting steps according to one embodiment ofthe invention for enhancing a wide-band image,

FIGS. 2A-2F illustrate examples of the application of the methodsdepicted in FIG. 1 to two images with both the unipolar and bipolarrepresentations of edges,

FIGS. 3A-3D provide examples of the application of two alternativeembodiments of the invention, where one embodiment employs two Hilberttransforms along two directions and the other employs four suchtransforms,

FIG. 4 shows a flow chart depicting an apparatus according to anembodiment of the invention,

FIG. 5 shows a human observer employing an apparatus according to anembodiment of the invention for the real-time viewing enhancement ofnatural scenes,

FIGS. 6A-6D show an original image and three enhanced versions of theoriginal image obtained according to an embodiment of the invention,where the image labeled “enhanced” employs both dark and bright lines,and the other two modified images employ only bright lines,

FIG. 7 shows one embodiment of the invention for illuminating thefeatures of an object in a natural scene,

FIGS. 8A-8B illustrate enhancement of images with different sizesaccording to the invention,

FIGS. 9A-9B, similar to FIGS. 8A-8B, show the enhancement of two imageswith different sizes according to an embodiment of the invention,

FIG. 10 shows an image processing system according to one embodiment ofthe invention for enhancing broadcast television images,

FIG. 11 is a flow chart of a sequence according to the invention anddepicts various steps for extracting luminance features of an image byemploying a plurality of oriented filters according to the teachings ofthe invention,

FIG. 11a is a flow chart of an exemplary sequence of steps forextracting luminance features of an image by employing a plurality offilters in the frequency domain according to the teachings of theinvention,

FIG. 12 illustrates an exemplary application of the method of FIG. 11,in which only one filter orientation was employed, to a natural imagefor extracting the luminance features of the natural image,

FIG. 13 illustrates a patient, suffering from a loss of peripheralvision, viewing a natural scene through a corrective viewer according tothe invention that provides the patient with a minified cartoon image ofthe scene,

FIG. 13a is a schematic diagram of the apparatus of FIG. 13,

FIG. 14 is a schematic diagram of night vision apparatus according tothe present invention that provides an observer with either a brightinfrared image or a contour image of the surrounding or both, and

FIG. 15 depicts a method according to the teachings of the invention forincreasing the field of view of a patient suffering from a central fieldloss by intra-ocular implantation of a telescope configured as aminifier into one eye of the patient.

ILLUSTRATED EMBODIMENTS

The flow chart of FIG. 1 shows various steps that an illustratedembodiment of the invention employs to modify an image represented byImage Data. The particular illustrated embodiment in step 10 applies ahigh pass filter in the spatial frequency domain to the image data toeliminate selected frequency components of the image. The high passfilter is typically constructed to retain frequency components thatcorrespond to a few cycles per image, e.g., sixteen cycles per image orhigher, and to discard components that correspond to lower frequencies.

The illustrated embodiment of FIG. 1 obtains the two-dimensional Hilberttransform of the filtered image data in step 12 by performing a sequenceof three operations on the filtered input image data. The firstoperation calculates the two-dimensional Fourier transform of thefiltered image data to obtain a transform function. The second operationcreates a modified transform function that vanishes over a selectedcontiguous half of the two-dimensional Fourier space of the transformedfiltered image data, and has values identical to those of the transformfunction of the previous operation in the other half. The thirdoperation applies an inverse Fourier transform to the modified transformfunction to obtain a complex function whose imaginary part correspondsto the Hilbert transform of the filtered image data.

An alternative practice of the operations of step 12 of the FIG. 1sequence, suited for manipulating an image data that is represented by atwo-dimensional matrix, obtains a discrete two-dimensional Hilberttransform of the image data by performing a different set of threeoperations. The first operation calculates a discrete two-dimensionalFourier transform of the image matrix to obtain a transform matrix. Thesecond operation sets the values of a selected half of the components ofthe transform matrix to zero to obtain a modified transform matrix, andthe third operation obtains the discrete inverse Fourier transform ofthe modified matrix to obtain a matrix whose imaginary part correspondsto the discrete Hilbert transform. As mentioned above, the use of thisalternative practice of the invention is particularly suited when theimage data is represented by a matrix of intensity values correspondingto the intensities of a two-dimensional set of pixels representing theimage.

There exists an inherent arbitrariness in the choice of the half of themodified transform matrix that is set to zero. One preferred embodimentof the invention sets the lower half of the transform matrix to zero toobtain the modified transform matrix. Another embodiment sets the upperhalf of the transform matrix to zero to obtain the modified transformmatrix. Yet, another embodiment sets the components below the diagonalof the matrix to zero and retains the rest. Setting a selected half ofthe transfer matrix to zero corresponds to obtaining the Hilberttransform along a particular direction. The choice of the direction ofthe Hilbert transform can depend on a number of factors such as, thepredominant orientation of the luminance features of the image and/orcomputational convenience. Subsequently, application of a discreteinverse Fourier transform to the modified transform matrix results inobtaining a matrix of complex numbers, the inverse modified transformmatrix, whose imaginary part corresponds to the discrete Hilberttransform of the filtered image data. Each choice of a modifiedtransform matrix, i.e., choosing which half of the transform matrix isset to zero, corresponds to obtaining a Hilbert transform along aparticular direction in the image plane, e.g., horizontal or vertical.The multiple panels of the step 12 are meant to depict such selectionsof different orientations. Each orientation can preferentially extractluminance features lying mainly along that orientation.

Those skilled in the art appreciate that a similar arbitrariness existsin creating a two-dimensional Hilbert transform if a continuous functionrather than a matrix represents the image data. This arbitrariness stemsfrom the choice of the half of the two-dimensional plane on which thevalues of a modified transform function, described above, are zero.

FIG. 1 shows that a step 14 of the illustrated embodiment constructs aso-called energy matrix by performing four successive operations on theimage matrix and the discrete Hilbert transform of the image matrix,represented by the imaginary part of the modified transform matrix. Thefirst operation obtains the square of the image matrix. The secondoperation obtains the square of the discrete Hilbert transform matrix.The third operation adds the square of each matrix to the square of theother, and the fourth operation computes the square root of thesummation to obtain the energy matrix. Those skilled in the art willunderstand that the same sequence of operations provides an energyfunction when applied to continuous functions, rather than to discreterepresentations of such functions by matrices. Those skilled in the artwill also appreciate that there are methods, other than the Fouriermethod described above, for obtaining two-dimensional Hilbert transformsfor use in the practice of the invention. A peakfinder step 16 of theillustrated embodiment, shown in FIG. 1, receives the energy matrix, andprovides a number of maximum points of the energy matrix to subsequentsteps of the illustrated embodiment by performing the following threeoperations. The first operation locates the local extrema of the energymatrix, i.e., local maxima and minima, by computing a two-dimensionalgradient of the energy matrix and finding points at which the gradientvanishes, according to known methods in the art. The second operationdetermines whether such a point corresponds to a local maximum or alocal minimum of the energy matrix, by employing the second derivativeof the energy matrix at each located extremum or by employing otherknown methods, and retains the maximum points and discards the minimumpoints. The third operation compares the intensity of the maxima of theenergy matrix or the intensity of the second derivative of the energymatrix at each selected maximum with a pre-defined threshold value, andretains only points whose intensities exceed the threshold value.

The maxima that the peakfinder step selects correspond to three types offeatures in the original image. They can either indicate the locationsof minimum or maximum intensities, i.e., bars, or transitions betweenregions of varying intensities, i.e., edges. In the case of edges, thepolarity of the transitions for a pre-defined direction, e.g., left toright and top to bottom, can not be readily gleaned from the energymatrix.

One implementation of the illustrated embodiment chooses an arbitraryunipolar representation of the located maxima, i.e., it represents themaxima as bright or dark dots regardless of whether they correspond tobars or edges and also regardless of their actual polarities. Anotherimplementation that opts for a bipolar representation employs dark andbright dots, symmetrically disposed with respect to the locations of themaxima, to display the maxima. One such implementation chooses anarbitrary polarity for displaying the dark and bright dots thatrepresent edges based on the arbitrary selection of the orientation ofthe Hilbert transform, whereas a different implementation examines theimage data to choose a polarity that corresponds to that in the image.

FIG. 1 shows that a phase detector step 18 of the illustrated embodimentreceives the image data and locations of the extrema of the energymatrix to provide the option of examining the image data in a selectedneighborhood of each pixel corresponding to a maximum of the energymatrix, to determine whether such a pixel corresponds to a bar or anedge in the image. In addition, this step determines polarities of thetransitions in luminance at points corresponding to edges in the image.

Further reference to FIG. 1 illustrates that a step 20 of theillustrated embodiment utilizes the information that the step 16supplies, and also in some implementations the information that the step18 supplies, to create contour lines corresponding to selected visuallyrelevant luminance features of the image by performing three operations.The first operation creates a two-dimensional map of dots correspondingto the selected maxima. The second operation, which is optional, canalter the widths of the dots through convolution with a tapered window,e.g., a Gaussian function with a pre-determined width, or alternativelyenhance the dots by changing the degree of their luminance. The thirdoperation, which is also optional, joins proximal dots to create contourlines in a manner known in the art.

An enhanced image construction step 22 of the illustrated sequencesuperimposes a display of the contour lines output from the step 20,onto the original image. The resultant enhancement can be unipolar orbipolar, and it can have an arbitrary polarity or a polarity thatcorresponds to that of the feature in the actual image.

Thus, the illustrated embodiment, employing steps 10 through 22 of FIG.1, produces an enhanced version of the original image by accentuatingthe visually relevant luminance features of the image.

FIG. 2 illustrates the results of the application of the method of theembodiment illustrated in FIG. 1 to two images. In particular, thedifferent views in FIG. 2 allow the comparison of unipolar and bipolaredge representations of the modified images. For example, views 2E and2F show that both the unipolar and bipolar displays represent thewrinkles on the forehead of the depicted subject, i.e., bars, as darklines. The views 2B and 2E show that the unipolar displays representedges, such as transitions in luminance at the boundary of the jacketand the face, as dark lines, whereas the views 2C and 2F show thatbipolar displays represent such transitions as dark and bright linepairs disposed symmetrically with respect to the center of thetransition. Furthermore, a comparison of the bipolar edgerepresentations in views 2C and 2F, with the views 2A and 2D of theunmodified images, readily illustrates that the chosen polarities of theedges correspond to the actual polarities in the unmodified images.

Referring back to FIG. 1, another embodiment of the invention combinesmultiple Hilbert transforms to produce a modified energy function of thefiltered image. For example, one implementation of this embodimentemploys the Hilbert transform step 12 to obtain two Hilbert transformsof the image data corresponding to two different orientations of axes inthe Fourier plane. In such an embodiment, the energy construction step14 creates two energy matrices corresponding to the two Hilberttransforms. The peak finder step 16, receives the energy matrices andobtains the extrema of the energy matrices. The optional phase detectorstep 18 determines the polarity of transitions of the luminance featuresof the image data corresponding to the extrema of the energy matrices,The contour construction step 20 receives the output of the peakfinderstep 16 and the phase detector step 18, and creates contours of allfeatures obtained through the multiple Hilbert transforms. The imageconstructor step 22 superimposes all these contours onto the originalimage to produce an enhanced image.

One advantage of employing multiple Hilbert transforms in the step 12 ofFIG. 1 sequence is that each transform results in a preferentialdelineation of luminance features that substantially lie in thedirection of the selected axes, in the two-dimensional Fourier plane,utilized to obtain the transform. Thus, superposition of luminancefeatures obtained from a set of Hilbert transforms results in betterenhancement of the image than superposition of features obtained fromonly one such transform.

FIG. 3 provides a comparison of two enhanced versions of an imageobtained by employing multiple Hilbert transforms in accord with theprocedure of FIG. 1. In particular, the view 3B of the original image 3Awas obtained by employing two Hilbert transforms in the step 12 of FIG.1, whereas the view 3D of the original image 3C was obtained byemploying four Hilbert transforms in the step 12.

FIG. 4 shows an image processing system according to an embodiment ofthe invention for implementing the procedure of FIG. 1. A digitizer 24,responsive to an analog image data, supplies a digitized image datacorresponding to an input image to a microprocessor or a programmeddigital computer 26. The microprocessor or the computer is programmed toperform a sequence of operations corresponding to the steps 10, 12, 14,16, 18, 20, and 22 of the illustrated embodiment of FIG. 1 on thedigitized inputted image data to obtain data corresponding to anenhanced version of the input image. A display unit 28, e.g., a monitoror a viewer, receives the output of step 22 to present an enhancedversion of the input image to an observer.

FIG. 5 shows an illustrated embodiment of the invention according to theapparatus of FIG. 4 that allows real-time image enhancement ofreal-world scenery. Reference to FIG. 4 shows a human observer wearingapparatus according to the invention and which includes a video camera,preferably a digital camera, that provides image data corresponding tothe natural scene 29. The apparatus transfers the digital image data toa dedicated microprocessor, programmed to extract the bars and edges inthe image and to provide a contour map of the extracted bars and edgesaccording to the method of the present invention. The processortransfers the contour map to a video display module that projects themap on two partially transparent screens positioned in the front of theobserver's eyes, known in the art as a see-through head-mounted display.This projected map 29A allows the observer to view the natural scenewith an enhancement of its distinctive features. The video displaymodule can be, for example, that employed in a see-through head mounteddisplay unit sold under the trade name i-glasses by Virtual I-O companyof Seattle, Wash., U.S.A. Alternatively, the visual display module of asee-through head mounted display unit sold under the trade nameglasstron by Sony corporation of Japan can be adapted for use in thepresent invention.

Due to the limited visual field of the display device, the apparatus ofFIG. 5 is typically configured to enhance only a portion of anobserver's field of view, e.g., the central portion. Such apparatuscontinuously enhances the central portion of the observer's field ofview as the observer turns her eyes from one part of a natural scene toanother. Other parts of the natural scene are enhanced when the observerturns her head towards them.

FIG. 6 depicts various modified versions of a natural scene, employingdifferent polarities, according to the methods of the present invention.The image labeled “enhanced” 6B uses a bipolar representation. Thebottom images 6C and 6D use only positive polarities, i.e., brightlines. The use of bright lines is the practical method for enhancing thereal world view. While the image labeled “positive only” uses brightlines to represent only features that correspond to positive polarity inthe original image, the image labeled “all positive” uses bright linesto represent all features of interest, including those that arerepresented by dark lines in the bipolar enhanced image.

FIG. 7 shows an image enhancement system according to the invention thatilluminates the features of objects in a natural scene which correspondto the luminance features in an image of such objects, e.g., bars andedges. A digitizer 30 digitizes an image of a natural scene. Amicroprocessor or a programmed digital computer 32 obtains the locationsof the bars and edges in the image and supplies this data to a lightsource guidance system 34. The guidance system directs the light sourceto illuminate the locations in the natural scene corresponding to thebars and edges in the image of the scene. One implementation of thisembodiment for laser shows and for similar applications, employs laserbeams scanned over the locations of the bars and edges to illuminatethese features.

One advantage of the methods of this invention is the ability to changethe size of an image while retaining a selected degree of enhancement.FIGS. 8 and 9 illustrate this aspect of the invention by presentingimages of different sizes and their enhanced counterparts. Inparticular, these figures show images and inserts that differ in theirrespective areas by a scale factor, illustrated on a factor of sixteen.An examination of these two figures illustrates that the application ofthe methods of the invention to the smaller inserts produces visuallyenhanced results similar to those obtained for the full size images.

One application of the present invention relates to enhancingtransmitted images, e.g., broadcast television images. FIG. 10 depictsan image processing system according to the invention for optionalenhancement of such images. The illustrated system has an encoder 36 ata central broadcasting station for forming an enhancement signal bysupplanting an image signal with information needed for enhancing theimage, i.e., by identifying the pixels that need to be modified and byidentifying the degree of modification of each pixel. The broadcast ofthe enhancement signal is manageable because the methods of theinvention modify only a small fraction of the pixels that constitute theimage, thus requiring minimal expansion of the transmission bandwidth. Atransmitter 38 sends the original image data and the enhancinginformation to a receiver 40 that optionally uses the information forenhancing the received image. In an analog television system, theenhancing information is transmitted to the receiver during theso-called “blank time,” in a manner similar to that utilized forproducing captions for the hearing impaired, to produce an enhancedimage for viewers with visual impairments. Further reference to FIG. 10shows a decoder 36 a that decodes the encoded signal and a switch 42that controls whether the original image data or an enhanced image issent from the decoder to a display unit 44.

The image-enhancing practices of the invention are not limited to grayscale pictures, and can also be employed to enhance color pictures.

One preferred practice of the invention employs a plurality of orientedfilters for the detection of luminance features, such as edges and/orbars, in complex natural images. In particular, the flow chart of FIG.11 shows a sequence of steps that one preferred embodiment of theinvention, which utilizes oriented filters, employs to extract featuresof a natural image. The illustrated embodiment in step 46 applies atleast a band pass filter in spatial domain to received image data, suchas a digital image of a natural scene, to obtain a filtered image. Avariety of band pass filters suitable for use in practicing step 46 areknown in the art. One example of such a filter includes atwo-dimensional spatial filter whose angular profile is smoothed by aGaussian multiplier. See E. Peli, “Adaptive Enhancement Based On AVisual Model”, Optical Engineering vol. 26 No. 7, pp. 655-680 (1987).Other filters known in the art, such as cosine log filters, can also beemployed. See E. Peli, “Contrast In Complex Images”, Journal of theOptical Society of America A, vol. 7, No. 10, pp. 2032-2040 (1990).

The illustrated filters 1 through n are centered at progressively higherfrequencies. One preferred practice of the invention selects the centerfrequencies of the filters to be in a mid-frequency range, i.e., therange of frequencies does not include very low or very high frequencies.For example, for processing an image having 512 pixels, the selectedfrequencies can be in the range 16 cycles/image to 128 cycles/image.

In addition to employing filters centered at different frequencies, onepreferred practice of the invention also employs a plurality of spatialfilters oriented along different directions in the image plane, e.g.,horizontal, vertical or diagonal. For example, application of a set ofillustrated filters 1 ₁ through 1 _(n), each having a differentorientation and the same center frequency as that of filter 1, to theimage data produces a set of filtered images. Further, FIG. 11illustrates filers 2 ₁ through 2 _(n), and m₁ through m_(n) that havethe same center frequencies as those of the filters 2 and m,respectively, and have a plurality of different orientations.Application of such selected filters to the image data produces a set ofspatially filtered images.

A polarity assignment step 48 receives the filtered images and assignsluminance scales to the pixels of each filtered image according to thefollowing criteria, to obtain an assigned image. The assignment step 48represents each pixel having an amplitude whose absolute value is belowa selected threshold, determined for example by the visual responsefunction, by a gray scale or a zero value, and represents each pixelhaving a negative amplitude whose absolute value is above the selectedthreshold by a dark scale or −1 value. Each pixel having a positiveamplitude whose absolute value is above the selected threshold isrepresented by a light scale or +1 value. The illustrated embodimentapplies a number of such filtering and assignment steps, 46 a, 48 a, 46b, 48 b, 46 n, 48 n having different filters, but the same assignmentprocedure, in parallel to the image data to obtain a plurality ofassigned, filtered images. For example, the illustrated embodiment instep 46 b applies bandpass filters, 2, 2 ₁, . . . , 2 _(n), selected tohave a spatial frequency response that is different from that of thefilters applied in step 46 a, to the image data. For example, thefilters in step 46 b can be selected to have a frequency response thatis centered at a higher frequency than that of the filters in step 46 a,i.e., the filters in step 46 b do not suppress the high spatialfrequencies of the image to the degree that the previous filters do.Application of the filters of the step 46 b to the image data producesother filtered images that an assignment step 48 b receives. Theassignment step 48 b assigns dark, gray, or light scales to the pixelsof each filtered image obtained through the step 46 b, based on the samecriterion as that employed by the assignment step 48 a. Thus, a set ofone or more filtering and assignment steps, applied in parallel to theinput image data, provides a plurality of filtered, assigned images.

A construction step 50 receives data corresponding to all filtered,assigned images, to construct features of the original image. Inparticular, the construction step 50 inspects each pixel in each of theassigned images, and represents a pixel by a light scale if the pixel isrepresented by a light scale in all assigned images, and represents apixel by a dark scale if the pixel is represented by a dark scale in allof the assigned images. The remaining pixels are represented by a grayscale. The construction step 50 employs the foregoing procedure employedto represent features of the image by dark, light, and gray dots. Thus,the construction step 50 provides a contour image corresponding to eachfiltered image. Such contour images can be utilized individually.Alternatively, a combiner step 50 a provides a composite contour imageby combining a selected number of the contour images received from theconstruction step 50.

The above method for extracting features of an image advantageouslyobtains the polarity of edges, i.e., a transition from light to dark,automatically and without inspection of the original image. It should beunderstood that the above method can employ both spatial domain andfrequency domain filters. In particular, those skilled in the art willrecognize that filters in the frequency domain can be employed invarious filtering steps of FIG. 11. Employment of such frequency domainfilters necessitates transforming the image data from the frequencydomain to the spatial domain, to obtain a modified natural image thatdelineates the features of the image. In particular, with reference toFIG. 11a, a transformation 44′, such as a Discrete Fourier Transform ora wavelet transform or other known transforms for transforming a digitalimage from the spatial domain to the frequency domain, of the image datato the frequency domain provides a frequency domain version of the imagedata. Application of filtering steps 46′a, 46′b, . . . , 46′n, to thefrequency domain image data, for example through deleting selectedfrequency components, produces a plurality of filtered frequency-domainimages. A transformation 44′a, such as Inverse Discrete FourierTransform, from the frequency domain to the spatial domain applied tothe filtered frequency-domain image data provides a plurality offiltered images in the spatial domain. A plurality of assignment steps48′a, 48′b, . . . , 48′n, assign dark, light, and gray scales to thepixels of the filtered images in the spatial domain by employing thesame criterion described in connection with the assignment step 48 ofFIG. 11. A construction step 50′ obtains a contour image of the originalimage data in a manner identical to that described in connection withthe step 50 of FIG. 11. A combiner step 50′a optionally combines aselected number of the contour images received from the constructionstep 50, to produce a composite contour image.

As an illustration of the application of the above methods to a naturalimage, FIG. 12 shows a natural image 52 whose features are extractedthrough a filtering procedure according to the method of FIG. 11. Thefiltering procedure employs four horizontally oriented spatial frequencyfilters. In particular, application of the first oriented spatialband-pass filter, centered at a frequency of 16 cycles/image, andsubsequently a polarity assignment to the natural image 52 provides afiltered assigned image 52 a in which the pixels having positiveamplitudes whose absolute values are above a selected threshold arerepresented by light dots, and pixels having negative amplitudes whoseabsolute values are above the selected threshold are represented bylight dots. The gray areas in the filtered image 52 a correspond to thepixels having amplitudes whose absolute values are below the selectedthreshold.

The filtered image 52 a illustrates features corresponding to lowfrequency spatial variations in the natural image. Application of aseparate filter, centered at a frequency of 32 cycles/image, and apolarity assignment procedure to the natural image 52 provides a secondfiltered, assigned image 52 b, in which the pixels are represented bygray, dark, or light dots according to the same procedure as thatemployed to obtain the filtered image 52 a. The filtered image 52 bcontains spatial variations at higher frequencies than those in thefiltered image 52 a. Application of third and fourth filters, havingcenter frequencies of 64 and 128 cycles/image, and assignment steps tothe natural image 52 provides filtered, assigned images 52 c and 52 d,respectively.

A constructed image 52 e that shows the features of the natural image isobtained by inspecting each pixel of the filtered assigned images 52 a,52 b, 52 c, and 52 d. If the pixel is represented by a light dot in allof the four assigned images, the pixel is represented by a light dot inthe constructed image 52 e. If the pixel is represented by a dark dot inall of the four assigned images 52 a, 52 b, 52 c, and 52 d, the pixel isrepresented by a dark dot in the constructed image 52 e. The remainingdots are represented by a gray scale. It should be understood that anynumber of filters can be employed according to the above method of theinvention to obtain a desired contour image. For example, in certainapplications, it may be desirable to apply a number of high frequencyfilters to capture contrast variations in the natural image that occurover a small spatial scale, i.e., high-frequency variations, whereas inother applications, it may be desirable to include many low frequencyfilters.

In one aspect, the present invention allows a patient having arestricted peripheral vision to locate objects quickly that lie outsideher instantaneous field of vision. The term instantaneous field ofvision as employed herein refers to a patient's peripheral vision whilethe patient is viewing a scene without eye and/or head movements. Manypatients suffer from a loss in peripheral field of view due to a varietyof conditions such as end-stage Glaucoma or Retinitis Pigmentosa (“RP”).Although many jurisdictions consider patients having a central field ofview of less than 20 degree diameter as legally blind, considerabledifficulties arise in the mobility of a patient when her field of viewshrinks to about 5 degrees in diameter or less.

A conventional approach for improving the field of view of such patientsemploys an optical reverse telescope that provides a spatiallycompressed, i.e., minified, view of a natural scene to one eye of thepatient. The minified view presents a representation of a wider field ofview into the limited visual field of the patient. One drawback of thisconventional methodology is that the resolution of the minified image istypically considerably lower than that of the non-minified image.

One practice of the invention for expanding a patient's field of viewsolves the foregoing and other shortcomings of the conventional methodsby providing at least one eye of the patient with a minified view of anatural scene whose features have been extracted, and typicallyenhanced, according to the teachings of the invention. Reference toFIGS. 13 and 13a shows a patient 54, having functional eyes both ofwhich suffer from a loss of peripheral vision, viewing a natural scene56 through a corrective viewer 58. A small ellipse 56 a indicates thefield of view of the patient, for example a 5 degree field. Theschematic diagram of FIG. 13a illustrates that the viewer 58 of FIG. 13includes an image former 60, such as a digital camera equipped with aminifier, such as a reverse optical telescope, to obtain a minifiedimage of the natural scene. A processor 62 receives the minified imagedata, extracts luminance features of the image, such as edges and bars,according to the teachings of the invention, to obtain datacorresponding to a contour image 64, shown in FIG. 13, that delineatesfeatures of the natural scene. Such an image of the natural scene isherein referred to as a cartoon image. The image 64 can, for example,present a 25-degree minified image of the contours of objects in anatural scene that spans a 100-degree field of view. A display module66, shown schematically in FIG. 13a, projects the cartoon image 64,shown in FIG. 13, onto a transparent screen 68 of the viewer 58 of FIG.13, i.e., a see-through screen, disposed in front of at least one of thepatient's eyes. A patient having a 5 degree field of view can, at anyinstant, see a 20 degree slice of the natural image in contours byviewing the cartoon image.

A patient can advantageously scan the cartoon image 64 quickly in searchof objects of interest. Slight eye movements allow the patient to locateobjects in the cartoon image. Once the patient identifies a cartoonimage of an object, the patient can then locate the actual object in thenatural scene by eye movements, without any head movements, or with acombination of eye and head movements. In particular, the viewer 58 ispreferably configured not to block the peripheral vision of the patient.Thus, once the patient identifies a cartoon image of an object that liesoutside her instantaneous peripheral vision, the patient can locate theactual object by moving her eyes toward the direction indicated by thecartoon image of the object. Alternatively, the patient can locate theobject in the natural scene by a combination of eye and head movements.Thus, the apparatus 58 advantageously allows a patient with restrictedperipheral vision to locate quickly objects outside her instantaneousfield of view, and to obtain a high-resolution view of such objects byviewing them directly.

Patients having RP typically also suffer from night blindness or loss ofnight vision. One practice of the invention provides an infrared nightvision device that displays a bright image of a natural scene on onescreen, and a minified cartoon version of the image, obtained throughthe foregoing teachings of the invention, on the same or another screen.In particular, in one embodiment of the night vision device of theinvention depicted schematically in FIG. 14, an infrared camera 70obtains an infrared image of a natural scene, and a display module 72displays the image on a screen, preferably spectacle mounted or mountedto move with the movement of the patient's head, disposed in front of atleast one of the patient's eyes. A digitizer 74 digitizes the infraredimage, and provides the digitized data to a digital processor 76 thatobtains data corresponding to contours of the luminance features of theimage according to the teachings set forth above in reference to FIGS.1, 11, and/or 11 a. The display module 72 of FIG. 14 displays thecontour image on the same or on a separate screen, preferably spectaclemounted, disposed in front of the patient's eyes. The display module 72can, for example, employ a diode laser source and a set of mirrors, thatcan be selectively actuated, to trace out the contour features by thelaser beam in a known manner, for displaying the cartoon image. Theinvention can be employed with the combination of the two images, orwith either image alone. For example, switches 76 b and 76 c allow thedisplay module 72 to display the infrared image or the cartoon imageeither individually or in combination.

Such a night vision device advantageously enhances night mobility ofpatients who suffer from a loss of night vision and visual field loss.In addition to being useful for patients having night blindness, aninfrared night vision device according to the invention can have avariety of other useful applications. For example, such a night visiondevice can provide a driver of an automobile with a contour image of thesurrounding, by projecting the contours of objects in an image of thenatural scene, obtained for example by an infrared camera, onto thewindshield of the automobile. Such projection of the contours of theimage has certain advantages over projection of a bright image, obtainedby an infrared camera, onto the windshield. For example, during periodsof low ambient light intensity, e.g., night time, projection of a brightimage onto the windshield can lower the viewing contrast through thewindshield. A contour outline projected onto the windshield, however,does not cause such a lowering of the contrast. Moreover, a driver canhave a clear view of the surrounding while viewing the contour outlines,i.e., the contour outline does not partially block the driver's view.

Another aspect of the invention relates to the intra-ocular implantationof a telescope, configured as a minifier, in one eye of a patientsuffering from peripheral field loss. Such a telescope provides one eyeof the patient with a wide field of view to help the mobility of thepatient, while the other eye maintains the good central acuity. FIG. 15illustrates a minifier 78 implanted into one eye of a patient accordingto known methods in the art. The patient can obtain a one-to-one view ofa natural scene through the eye not having a minifier, and can obtain awider view of the natural scene through the minifier 78.

It will thus be seen that the invention attains the objectives set forthabove. Because certain changes in the above apparatus and processes canbe made without departing from the spirit or scope of the invention, theabove description is intended to be interpreted as illustrative and notin a limiting sense.

What is claimed is:
 1. A method for real-time modification of a naturalscene for a human observer, said method comprising the steps of:obtaining an image of said natural scene; extracting features of saidimage selected from the group consisting of local maximum luminance,local minimum luminance and transitions between regions of varyingluminance; manipulating said extracted features to obtain modifiedfeatures; and projecting said modified features onto a semi-transparentscreen disposed in front of an eye of said observer to enablesimultaneous viewing of both the natural scene and the modified featuresby said observer.
 2. The method of claim 1, wherein said image of saidnatural scene is a video image.
 3. The method of claim 1, wherein saidimage of said natural scene is an image within the visual spectrum. 4.The method of claim 1, wherein, in said projecting step, saidsemi-transparent screen is part of a television or movie screen.
 5. Themethod of claim 1, wherein, in said projecting step, saidsemi-transparent screen is part of a see-through head mounted display.6. The method of claim 1, wherein, in said projecting step, saidsemi-transparent screen is a plane between the observer and the scene.7. The method of claim 6, wherein said plane is an automobile or anairplane windshield.
 8. The method of claim 1, wherein said projectedmodified features are the same size as and aligned with said image. 9.The method of claim 1, wherein said modified features are represented bya single polarity of dots.
 10. The method of claim 1, wherein saidmodified features are displayed as a bipolar representation.
 11. Themethod of claim 1, wherein said natural scene is a low light environmentand wherein, further, said obtained image is an infrared or a radarimage.
 12. The method of claim 1, further comprising changing themagnification of said image.
 13. The method of claim 12, wherein themagnification of said image is changed before said extracting step. 14.The method of claim 12, wherein the magnification of said image ischanged after said extracting step.
 15. The method of claim 1, whereinthe extracting step includes: (a) applying a plurality of filters havingdifferent center frequencies to the image to obtain a plurality offiltered images, (b) assigning luminance scales to pixels of saidfiltered images by depicting a pixel by a light scale when absoluteamplitude of said pixel is above a selected threshold and said pixel hasa positive amplitude, depicting a pixel by a dark scale when absoluteamplitude of said pixel is above said selected threshold and said pixelhas a negative amplitude, and depicting the remaining pixels by a grayscale, and (c) constructing contour features of the image by depicting apixel of the image by a light scale if said pixel is represented by alight scale in all of said assigned images, depicting a pixel by a darkscale if said pixel is represented by a dark scale in all of saidassigned images, and depicting the remaining pixels by a gray scale. 16.The method of claim 15, wherein said step of applying a plurality offilters includes selecting said plurality of filters to compriseoriented filters.
 17. The method of claim 15, wherein said step ofapplying a plurality of filters includes selecting said plurality offilters to comprise spatial domain filters.
 18. The method of claim 17,wherein said step of applying a plurality of filters includes selectingsaid oriented filters to comprise spatial filters whose angular profilesare smoothed by a Gaussian multiplier.
 19. The method of claim 15,wherein said step of applying a plurality of filters includes selectingsaid filters to comprise Discrete Fourier Transforms.
 20. The method ofclaim 15, wherein said step of applying a plurality of filters includesselecting said filters to comprise a wavelet transform.
 21. The methodof claim 1, further including after said manipulating step minifyingsaid extracted features.
 22. The method of claim 9 further including thestep of connecting proximal dots to form a contour image.
 23. The methodof claim 10 further including the step of connecting proximal dots toform a contour image.
 24. The method of claim 13 wherein the change inmagnification is to minify said image.
 25. The method of claim 14wherein the change in magnification is to minify said image.
 26. Themethod of claim 1 wherein said projecting step includes encodingtransmitted television images using an enhancement signal by supplantingan image signal with said modified features.
 27. The method of claim 26further including the step of decoding said enhancement signal anddisplaying said supplanted image with said modified features.
 28. Amethod for real-time modification of a natural scene for a humanobserver, said method comprising the steps of: obtaining an image ofsaid natural scene; extracting features of said image selected from thegroup consisting of local maximum luminance, local minimum luminance andtransitions between regions of varying luminance; manipulating saidextracted features to obtain modified features, wherein said modifiedfeatures are represented by a single polarity of dots or said modifiedfeatures are displayed as a bipolar representation; and projecting saidmodified features onto a semi-transparent screen disposed in front of aneye of said observer to enable simultaneous viewing of both the naturalscene and the modified features by said observer.